Abstract
Objective
The aim of this study is to determine the prevalence of healthcare service delay behavior among earthquake survivors living in temporary accommodation centers during the long-term recovery period (between the 26th and 30th months) following the 2023 Kahramanmaraş earthquake, and to assess the impact of post-disaster living conditions on this behavior.
Method
This cross-sectional study was conducted with 596 adults living in temporary accommodation centers in Hatay between May 1 and August 30, 2025. The study identified factors affecting healthcare utilization using Andersen’s Behavioral Model. Data were collected using an Information Form and the Healthcare Demand Procrastination Scale. Data analysis was performed using descriptive statistics, independent samples t-tests, one-way ANOVA, and multivariate linear regression.
Results
The study found that the prevalence of healthcare delay behavior was 48.83%. In the regression analysis, healthcare service delay behavior was found to be influenced by predisposing factors, enabling factors, and need factors (p < 0.05). The established model accounted for 27.3% of the variance in healthcare delay behavior (R² = 0.273).
Conclusion
The findings indicate that nearly two years after the 2023 Türkiye earthquake, healthcare delay behavior remains highly prevalent among survivors living in temporary accommodation centers. This persistent delay reflects ongoing structural and access-related inequalities in post-disaster healthcare delivery. The results underscore the importance of strengthening accessible, continuous, and trauma-informed healthcare services, particularly in vulnerable and displaced populations, to reduce long-term disparities in healthcare utilization.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12889-026-28044-2.
Keywords: Earthquake victims, Healthcare service delay behavior, Post-disaster recovery, Türkiye, Long-term effects
Introduction
Disasters are social crises that profoundly affect not only the physical environment but also individuals’ lifestyles, psychological well-being, health behaviors, and the functioning of the health system [1–3]. Following destructive disasters such as earthquakes, access to healthcare is often disrupted [4, 5], and individuals may exhibit healthcare delay behavior [6]. Healthcare service delay behavior is defined as individuals delaying or completely postponing seeking professional help despite feeling a health need for various reasons [7]. The prevalence of healthcare delay behavior is related to both sociodemographic determinants and the structure of healthcare systems. Sociodemographic health determinants such as age, income level, educational status, employment status, and health insurance have a significant impact on access to healthcare services [8, 9]. In addition, the structure of healthcare systems, including access to services, insurance coverage, healthcare policies, and resource distribution, may also influence healthcare utilization behaviors [10–12]. Addressing these factors effectively is essential for improving access to healthcare services and reducing inequalities among different population groups.
One of the most widely used theoretical approaches for explaining healthcare utilization is Andersen’s Behavioral Model, which was first developed in 1968 and subsequently revised [13, 14]. The model explains healthcare utilization through the interaction of predisposing, enabling, and need factors and has been widely used to evaluate inequalities in healthcare access and utilization [15–17]. Predisposing factors include demographic characteristics, health beliefs, previous experiences, and attitudes toward healthcare utilization.
Enabling factors refer to structural conditions such as income, employment, health insurance, and physical or financial access to healthcare services. Need factors involve individuals’ perceived or evaluated health status and healthcare needs [14–19].
Healthcare delay behavior can also be understood within this framework, as delayed care-seeking is influenced by the same determinants that shape healthcare utilization.
Predisposing characteristics such as age and health perceptions, enabling conditions such as financial and physical access to services, and increased health needs after disasters may jointly affect whether individuals seek timely healthcare or postpone care. Therefore, Andersen’s Behavioral Model provides an appropriate framework for examining healthcare delay behavior in post-disaster contexts.
The model has been applied in various fields, including emergency care, mental health services, dental care, and prenatal healthcare [20–23]. Because it addresses both individual and structural determinants simultaneously, the model offers a useful framework for examining healthcare utilization behaviors in disaster contexts [24].
Following earthquakes, changes in individuals’ demographic and socioeconomic conditions may limit physical and financial access to healthcare services [25], while disruptions in infrastructure, healthcare personnel, and healthcare delivery systems may weaken enabling factors [26]. At the same time, difficulties in managing chronic diseases and increased healthcare needs may intensify need factors [27, 28]. This multidimensional interaction may contribute to delayed or postponed healthcare utilization during the post-earthquake period.
The 2023 Kahramanmaraş earthquakes in Türkiye displaced millions of people, and many individuals continue to live in temporary accommodation centers [29]. These living conditions have created not only physical health risks but also long-term effects on healthcare utilization behaviors. Although previous studies have examined healthcare access immediately after disasters, evidence regarding healthcare delay behavior during the long-term recovery period remains limited [30]. Delayed healthcare utilization during prolonged post-disaster recovery may worsen existing health conditions and increase unmet healthcare needs. In particular, little is known about how post-disaster living conditions influence delayed healthcare utilization among individuals who continue to live in temporary accommodation settings months after a major earthquake.
Given the prolonged displacement and ongoing living difficulties following the 2023 Kahramanmaraş earthquakes in Türkiye, understanding delayed healthcare utilization has become increasingly important. However, evidence regarding healthcare delay behavior during the long-term recovery period remains scarce. Therefore, this study aimed to determine the prevalence of healthcare delay behavior among individuals living in temporary accommodation centers 26–30 months after the earthquake and to examine the predisposing, enabling, and need factors associated with this behavior within the framework of Andersen’s Behavioral Model.
Research question
What is the prevalence of healthcare service delay behavior among earthquake survivors living in temporary accommodation centers during the long-term recovery period after the earthquake (between the 26th and 30th months)?
What are the predictors of healthcare delay behavior among earthquake survivors living in temporary accommodation centers during the long-term recovery period (between the 26th and 30th months) following an earthquake?
Method
Design
This study is cross-sectional in nature. The study was planned and reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for reporting observational studies.
Population and sample
The study population consisted of adults aged 18 years and older who were temporarily residing in accommodation centers in Hatay province and were directly affected by the 2023 Kahramanmaraş earthquakes. The population size could not be determined with certainty due to factors such as the constant relocation of individuals in accommodation centers, outdated registration systems, and the absence of some individuals from official records.
A non-probability snowball sampling method was used. Initial participants were identified in collaboration with local field coordinators and accommodation center administrators, and subsequent participants were recruited through referrals within the same settings.
The sample size was calculated using a proportion estimation formula. The formula n = Z2 × p × (1 − p) / d2 was applied, with a 95% confidence level (Z = 1.96), a margin of error of 0.05, and a prevalence of healthcare service delay behavior of p = 0.773 in the literature [31]. The calculations yielded an initial sample size of approximately 270 participants. Due to field conditions, a 20% non-response rate was anticipated, increasing the sample size to 338 participants.
Given the field-based recruitment strategy and the likelihood that participants were recruited from the same accommodation centers, a design effect of 1.25 was applied as a conservative adjustment to account for potential intra-cluster correlation and homogeneity among participants within shared living environments.
The initial sample size was adjusted accordingly, resulting in a final target sample size of approximately 423 participants. Finally, data were collected from 596 participants, exceeding the minimum required sample size and providing sufficient statistical power for multivariate analyses. Because participants were recruited through referrals within the same accommodation settings, selection bias may have occurred and the sample may not fully represent all earthquake survivors residing in temporary accommodation centers.
The participants’ age ranged from 18 to 92 years (mean = 39.49 ± 15.55), and only adults (≥ 18 years) were included in the study because healthcare utilization behavior and decision-making processes may differ between adults and minors.
Inclusion criteria for the study: being 18 years of age or older, residing in one of the temporary accommodation centers in Hatay province, having experienced the 2023 Kahramanmaraş earthquake, being able to read and understand Turkish, and agreeing to participate in the study voluntarily. Exclusion criteria for the study are: having a hearing or visual impairment, having a serious cognitive impairment or being under active psychiatric treatment to a degree that prevents communication, and incomplete completion of the questionnaire form.
Data collection
Data collection took place between May 1 and August 30, 2025, approximately 26–30 months after the earthquake, following the necessary ethical committee approval and official permits.
Data were collected through face-to-face contact using self-administered questionnaires under researcher supervision. Interviews were conducted in suitable environments where participants felt comfortable, such as container seating areas or shaded areas designated in front of the administration building. Each interview lasted an average of 15–20 min. Participant confidentiality was ensured, and no identifying information was included in the questionnaires.
Prior to data collection, coordination was established with the Hatay Provincial Health Directorate, the Provincial Disaster and Emergency Management Directorate (AFAD), and the management of the Temporary Accommodation Centers. Field planning was carried out, and data collection points were determined according to the size and layout of each settlement area.
The purpose and scope of the study were explained to the participants, and a written Informed Consent Form was obtained on a voluntary basis. While literate participants completed the questionnaire forms independently, the researchers read the questions verbatim without any guidance and recorded the responses for the 65 illiterate participants (10.9%) to ensure standardized data collection. Limited, non-directive assistance was also provided to other participants who needed help marking their responses, particularly older adults.
The data collection tools used were the Information Form and the Healthcare Demand Procrastination Scale.
Information form
The Information Form was developed by the researchers based on the relevant literature. It consists of 24 items assessing participants’ sociodemographic characteristics (age, gender, marital status, education level, income status, employment status, etc.), duration of residence in container housing, health status, and access to healthcare services. The prevalence of healthcare delay behavior was determined by the question: “Have you delayed seeking healthcare in the last 6 months despite needing it?” [25, 32–34]. The English version of the Information Form is provided as Supplementary Material.
Healthcare demand procrastination scale
The Healthcare Demand Procrastination Scale was developed by Söyler et al. to determine individuals’ tendency to delay seeking healthcare services. The scale consists of 11 items and three subscales: self/individual remedy search (items 1–3), avoidance (items 4–7), and not taking action (items 8–11). Item 10 is reverse-coded during scoring. Responses to the scale items are rated on a 5-point Likert scale ranging from 1 = Strongly disagree to 5 = Strongly agree. Each subscale and total score is calculated by taking the average of the relevant items.
The minimum score obtainable from the scale is 1, the maximum score is 5, and the median score is 3. Higher scores indicate an increased tendency to delay healthcare demand. In the study by Söyler et al., the overall internal consistency coefficient of the scale was found to be 0.85 [35]. In this study, the overall Cronbach’s alpha value of the scale was calculated as 0.91; 0.87 for self/individual remedy search, 0.91 for avoidance, and 0.59 for not taking action.
Variables of the study
In this study, the dependent variable is the healthcare service delay behavior of earthquake victims, which was measured using the Healthcare Demand Procrastination Scale. The prevalence of healthcare delay behavior was determined using the yes/no question asking whether participants had delayed healthcare services in the last 6 months despite needing them. In contrast, the outcome variable used in the regression analyses was the total mean score of the Healthcare Demand Procrastination Scale, which reflects individuals’ tendency toward delaying healthcare utilization.
The independent variables were classified based on Andersen’s Behavioral Model. In line with the model, the variables were addressed under three main headings: predisposing, enabling, and need factors affecting healthcare utilization. In this context, the individual’s sociodemographic characteristics, such as age, gender, marital status, family type, and educational status, were evaluated as predisposing factors. Furthermore, prior experience of disasters before the Kahramanmaraş earthquake was evaluated as a predisposing factor, as it reflects the individual’s pre-existing experiences and attitudes towards healthcare service utilization in line with the model. The number of people living in containers, the length of stay in temporary accommodation centers, income status, and the impact of social support on health-related decisions were considered as enabling factors affecting an individual’s access to healthcare services. Furthermore, difficulties in making appointments, problems finding a doctor, and difficulties in accessing healthcare facilities were considered enabling factors because they reflect structural and environmental barriers that limit actual access, regardless of the availability of healthcare services. Additionally, the presence of chronic disease, receipt of psychological support after the earthquake, perceived health status, tobacco and alcohol use, and regular exercise were classified as need factors reflecting an individual’s health needs.
However, injury status during the earthquake was evaluated as a need factor because it reflects acute health needs.
Data analysis
The analysis of the study data was performed using the IBM SPSS Statistics 27.0 software.
Descriptive statistics included frequency (n), percentage (%), mean, and standard deviation values. The normality of the data distribution was assessed using skewness and kurtosis values, which were within the ± 1.5 range, indicating that the data were normally distributed.
The total mean score of the Healthcare Demand Procrastination Scale was used as the dependent variable in the regression analyses. Although the scale consists of Likert-type items, the total scale score was treated as a continuous variable because it was derived from multiple items and demonstrated approximately normal distribution, consistent with common practices in behavioral and health sciences research.
To identify the factors affecting healthcare demand postponement behavior, pairwise comparisons were first made between the independent variables and the dependent variable; the independent samples t-test was used for comparisons between two groups, and the one-way ANOVA test was used for comparisons between more than two groups. The Tukey post-hoc test was applied to the significant one-way ANOVA results. Variables found to be significant in the pairwise analyses were included in the multivariate model, and multiple linear regression analysis was applied to determine the predictors of healthcare service demand postponement behavior.
Before conducting the regression analyses, the assumptions of linearity, normality of residuals, multicollinearity, and homoscedasticity were evaluated and found to be acceptable. Specifically, multicollinearity was assessed using Tolerance and Variance Inflation Factor (VIF) diagnostics, and all regression models were validated alongside their 95% confidence intervals.
In the regression analysis, the reference categories of the binary variables were defined in a way that would facilitate the interpretation of the findings, taking into account the group means obtained in the pairwise comparisons and the literature. In all analyses, the statistical significance level was set at p < 0.05.
Ethics of study
Ethical approval for the study was obtained from the Scientific Research and Publication Ethics Committee of Social and Humanities Sciences at Hatay Mustafa Kemal University (Decision No: 22, Meeting No: 05, Date: 10.04.2025). In addition, the necessary institutional permissions were obtained from the Hatay Provincial Disaster and Emergency Management Directorate for the temporary accommodation centers where the data collection process was carried out. The purpose, scope, and voluntary nature of the research were explained to the participants, and a written Informed Consent Form was obtained. For illiterate participants, the study was explained in an understandable manner by the researchers, and informed consent was obtained from their legal guardians. Written permission was obtained from the relevant authors for the measurement tool used. Participation in the study was entirely voluntary, and no coercion or financial incentives were applied to participants. Considering the possible psychological effects of post-disaster conditions, the data collection process was conducted with a trauma-sensitive approach. Participants’ right not to answer questions that might remind them of past disaster experiences was specifically emphasized. Confidentiality and privacy were maintained in all interviews, and the research was conducted in accordance with the ethical principles of the Helsinki Declaration.
Results
This study was carried out with 596 earthquake survivors living in temporary accommodation centers. The average age of participants was 39.49 ± 15.55, ranging from 18 to 92 years. The average number of people living in each container was 3.58 ± 1.64, ranging from 1 to 12 individuals. The majority of participants had been living in temporary accommodation centers for 12 months or longer. Statistically significant relationships were found between age groups, gender, marital status, income status, and the presence of chronic diseases and healthcare service delay behavior (p < 0.05). The findings show that healthcare service delay behavior scores were significantly higher among individuals in all younger age groups (18–34, 35–49, and 50–64 years old) compared to those aged 65 and older, males, singles, individuals with an income lower than their expenses, and those without chronic diseases (Table 1).
Table 1.
Healthcare service delay behavior scores according to the participants’ sociodemographic and health-related characteristics (n = 596)
| Variables | Min.–Max. | Mean ± SD | Statistical test | p value |
|---|---|---|---|---|
| Age | 18–92 | 39.49 ± 15.55 | ||
| Number of people living in the container | 1–12 | 3.58 ± 1.64 | ||
| n (%) | Mean ± SD | |||
| Age (years) | ||||
| 18–34a | 282 (47.3) | 3.18 ± 1.01 |
F = 11.799 d < a,b, c |
< 0.001 |
| 35–49b | 169 (28.4) | 3.00 ± 1.01 | ||
| 50–64c | 81 (13.6) | 3.04 ± 1.01 | ||
| 65 + d | 64 (10.7) | 2.34 ± 1.04 | ||
| Sex | ||||
| Female | 333 (55.9) | 2.84 ± 1.01 | t = 4.899 | < 0.001 |
| Male | 263 (44.1) | 3.25 ± 1.02 | ||
| Marital status | ||||
| Married | 377 (63.3) |
2.89 ± 1.04 3.23 ± 1.01 |
t = -3.866 | < 0.001 |
| Single | 219 (36.7) | |||
| Family type | ||||
| Nuclear family | 570 (95.6) | 3.01 ± 1.04 | t = -0.878 | 0.380 |
| Extended family | 26 (4.4) | 3.19 ± 1.09 | ||
| Education level | ||||
| Illiterate | 65 (10.9) | 3.20 ± 0.92 | F = 0.797 | 0.496 |
| Elementary and middle school | 210 (35.2) | 3.03 ± 1.06 | ||
| High school | 152 (25.5) | 2.98 ± 1.02 | ||
| University and above | 169 (28.4) | 2.98 ± 1.07 | ||
| Employment status | ||||
| Employed | 227 (38.1) | 3.06 ± 1.05 | t = -0.764 | 0.445 |
| Unemployed | 369 (61.9) | 2.99 ± 1.03 | ||
| Income status | ||||
| Income less than expensesa | 354 (59.4) | 3.19 ± 1.04 |
F = 14.111 a > b,c |
< 0.001 |
| Income equal to expensesb | 192 (32.2) | 2.84 ± 0.94 | ||
| Income more than expensesc | 50 (8.4) | 2.51 ± 1.09 | ||
| Car ownership | ||||
| Yes | 257 (43.1) | 2.99 ± 1.04 | t = -0.683 | 0.495 |
| No | 339 (56.9) | 3.04 ± 1.04 | ||
| Duration of living in a container | ||||
| ≤ 6 months | 57 (9.6) | 3.08 ± 1.03 | F = 0.227 | 0.797 |
| 6–12 months | 78 (13.1) | 3.07 ± 1.09 | ||
| ≥ 12 months | 461 (77.3) | 3.00 ± 1.03 | ||
| Smoking | ||||
| Yes | 247 (41.4) | 3.08 ± 1.06 | t = 1.151 | 0.250 |
| No | 349 (58.6) | 2.98 ± 1.02 | ||
| Alcohol consumption | ||||
| Yes | 88 (14.8) | 3.07 ± 1.06 | t = 0.494 | 0.622 |
| No | 508 (85.2) | 3.01 ± 1.03 | ||
| Regular exercise | ||||
| Yes | 151 (25.3) | 3.02 ± 1.04 | t = 0.063 | 0.949 |
| No | 445 (74.7) | 3.02 ± 1.04 | ||
| Presence of chronic disease | ||||
| Yes | 178 (29.9) | 2.54 ± 1.00 | t = -7.702 | < 0.001 |
| No | 418 (70.1) | 3.22 ± 0.99 | ||
| Perceived health status | ||||
| Good | 193 (32.4) | 2.90 ± 1.03 | F = 1.852 | 0.158 |
| Moderate | 328 (55.0) | 3.06 ± 1.04 | ||
| Poor | 75 (12.6) | 3.13 ± 1.07 | ||
SD Standard Deviation, t Independent samples t-test, F One-way ANOVA (Tukey post-hoc). Bold values indicate statistically significant results (p < 0.05). Different superscript letters (a, b, c, d) indicate statistically significant differences between groups (p < 0.05). The 65+ group (d) is significantly lower than all other groups
Table 2 shows the participants’ healthcare service delay behaviors according to their exposure to disasters and access to healthcare services. Participants who had experienced another disaster before the Kahramanmaraş earthquake and those who were injured during the earthquake had significantly lower delay scores (p < 0.001).
Table 2.
Healthcare service demand delay behaviors of the participants according to their exposure to disasters and access to healthcare services (n = 596)
| Variables | n (%) | Mean ± SD | Statistical test | p value |
|---|---|---|---|---|
| Experienced any disaster before the Kahramanmaraş earthquake | ||||
| Yes | 135 (22.7) | 2.74 ± 1.13 | t = -3.619 | < 0.001 |
| No | 461 (77.3) | 3.10 ± 0.99 | ||
| Injury during the earthquake | ||||
| Yes | 155 (26.0) | 2.70 ± 1.16 | t = -4.157 | < 0.001 |
| No | 441 (74.0) | 3.13 ± 0.97 | ||
| Finding an appointment difficult | ||||
| Yes | 357 (59.9) | 3.22 ± 1.05 | t = 6.099 | < 0.001 |
| No | 239 (40.1) | 2.72 ± 0.94 | ||
| Transportation issues | ||||
| Yes | 238 (39.9) | 3.28 ± 1.05 | t = 5.022 | < 0.001 |
| No | 358 (60.1) | 2.85 ± 1.00 | ||
| Difficulty in finding a doctor | ||||
| Yes | 309 (51.8) | 2.84 ± 1.03 | t = -4.331 | < 0.001 |
| No | 287 (48.2) | 3.21 ± 1.02 | ||
| Health status deteriorating due to delayed healthcare services | ||||
| Yes | 67 (11.2) | 3.22 ± 0.96 | t = 1.704 | 0.089 |
| No | 529 (88.8) | 2.99 ± 1.05 | ||
| Impact of social support on healthcare decisions | ||||
| Yes | 250 (41.9) | 3.02 ± 0.99 | t = 0.051 | 0.959 |
| No | 346 (58.1) | 3.02 ± 1.08 | ||
| Receiving psychological support | ||||
| Yes | 131 (22.0) | 2.62 ± 1.02 | t = -5.031 | < 0.001 |
| No | 465 (78.0) | 3.13 ± 1.02 | ||
SD Standard Deviation, t Independent samples t-test. Bold values indicate statistically significant results (p < 0.05)
When factors related to access to healthcare services were examined, it was determined that individuals who experienced difficulties in scheduling appointments and accessing healthcare services had significantly higher healthcare service postponement behavior scores (p < 0.001).
However, individuals experiencing difficulties finding a physician were found to have lower delay behavior scores (p < 0.001). Individuals receiving psychological support had significantly lower delay behavior scores than those who did not (p < 0.001) (Table 2).
Table 3 shows the prevalence of participants’ healthcare service delay behavior and the scale total and subscale scores. The prevalence of participants’ healthcare service delay behavior was found to be 48.83%. The mean subscale scores related to health service delay behavior were 3.28 ± 1.25 for seeking individual remedies, 3.04 ± 1.31 for avoidance, 2.81 ± 0.92 for inaction, and the mean total score was 3.02 ± 1.04.
Table 3.
Prevalence of healthcare delay behavior among the participants and scale total and subscale scores (n = 596)
| Variables | n | % |
|---|---|---|
| Those who postponed healthcare services in the last 6 months | 291 | 48.83 |
| Those who did not postpone healthcare services in the last 6 months | 305 | 51.17 |
| Healthcare Demand Procrastination Scale | Mean ± SD | Min.–Max. (Received) |
|---|---|---|
| Self/individual remedy search | 3.28 ± 1.25 | 1–5 |
| Avoidance | 3.04 ± 1.31 | 1–5 |
| Not taking action | 2.81 ± 0.92 | 1–5 |
| Total | 3.02 ± 1.04 | 1–4.91 |
The healthcare service delay situation has been assessed based on the answers given to the question, “Have you delayed healthcare services in the last 6 months even though you needed them?”
SD Standard Deviation, Min Minimum, Max Maximum
In the linear regression analysis conducted to determine the factors predicting healthcare delay behavior, the variables were included in the model in three stages as predisposing, enabling, and need factors in line with Andersen’s Behavioral Model. Prior to evaluating these stages, the regression diagnostics confirmed that the multivariate model successfully met all necessary statistical assumptions. Multicollinearity was not a concern for any of the models, as all Variance Inflation Factor (VIF) values ranged between 1.04 and 3.53, remaining well below the conservative threshold of 5.0. Furthermore, the Durbin-Watson statistic was found to be 1.979, proving the absence of any autocorrelation within the residuals.Additionally, all estimated regression coefficients fell within statistically acceptable 95% confidence intervals.
Model 1, which included predisposing factors, was determined to be statistically significant (p < 0.001). This model explains 10.3% of the adjusted variance in healthcare delay behavior (Adj. R² = 0.103, p < 0.001). In Model 1, the 18–34 age group was determined to be the strongest predictor of healthcare service delay behavior. Healthcare service delay scores were found to be statistically significantly higher in all younger age groups (18–34, 35–49, and 50–64 years old) compared to the reference group of 65 years and older (p < 0.001). Being male and being single were identified as factors that increase delay behavior. Experiencing a different disaster prior to the Kahramanmaraş earthquake was found to be significant in the first model (p = 0.003) and the second model (p = 0.007), which included predisposing factors, but lost its significance when need factors were added to the model (p > 0.05) (Table 4).
Table 4.
Factors predicting healthcare service delay behavior: multivariate linear regression analysis
| Variables | Model 1 | Model 2 | Model 3 | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| B | SE | β | t | p | B | SE | β | t | p | B | SE | β | t | p | ||
| Predisposing factors | (Constant) | 1.990 | 0.142 | 14.064 | < 0.001 | 1.687 | 0.163 | 10.347 | < 0.001 | 2.318 | 0.181 | 12.835 | < 0.001 | |||
| Age group (Ref: ≥ 65 age group) | ||||||||||||||||
| 18–34 years old | 0.667 | 0.141 | 0.319 | 4.736 | < 0.001 | 0.688 | 0.133 | 0.330 | 5.186 | < 0.001 | 0.350 | 0.137 | 0.168 | 2.553 | 0.011 | |
| 35–49 years old | 0.583 | 0.147 | 0.252 | 3.982 | < 0.001 | 0.678 | 0.138 | 0.293 | 4.910 | < 0.001 | 0.400 | 0.139 | 0.173 | 2.879 | 0.004 | |
| 50–64 years old | 0.649 | 0.167 | 0.213 | 3.893 | < 0.001 | 0.640 | 0.156 | 0.210 | 4.093 | < 0.001 | 0.462 | 0.153 | 0.152 | 3.017 | 0.003 | |
| Sex (Ref: Female) | 0.346 | 0.083 | 0.165 | 4.167 | < 0.001 | 0.342 | 0.078 | 0.163 | 4.382 | < 0.001 | 0.249 | 0.076 | 0.119 | 3.265 | 0.001 | |
| Marital status (Ref: Married) | 0.241 | 0.090 | 0.111 | 2.670 | 0.008 | 0.202 | 0.085 | 0.094 | 2.380 | 0.018 | 0.190 | 0.082 | 0.088 | 2.327 | 0.020 | |
| Experienced a different disaster before the earthquake (Ref: No) | 0.289 | 0.098 | 0.116 | 2.932 | 0.003 | 0.254 | 0.094 | 0.102 | 2.716 | 0.007 | 0.108 | 0.092 | 0.043 | 1.166 | 0.244 | |
| Enabling factors | Income status (Ref: Income equal to expenses) | |||||||||||||||
| Income is less than expenses | — | — | — | — | — | 0.244 | 0.087 | 0.115 | 2.816 | 0.005 | 0.235 | 0.083 | 0.111 | 2.818 | 0.005 | |
| Income is more than expenses | −0.401 | 0.149 | −0.107 | −2.682 | 0.008 | −0.339 | 0.144 | −0.090 | −2.348 | 0.019 | ||||||
| Finding an appointment difficult (Ref: No) | — | — | — | — | — | 0.305 | 0.084 | 0.144 | 3.624 | < 0.001 | 0.328 | 0.081 | 0.154 | 4.047 | < 0.001 | |
|
Transportation issues (Ref: No) |
— | — | — | — | — | 0.316 | 0.085 | 0.149 | 3.716 | < 0.001 | 0.362 | 0.082 | 0.170 | 4.408 | < 0.001 | |
| Difficulty in finding a doctor (Ref: No) | −0.213 | 0.079 | −0.102 | −2.713 | 0.007 | −0.183 | 0.076 | −0.088 | −2.413 | 0.016 | ||||||
| Need factors | Presence of chronic disease (Ref: No) | — | — | — | — | — | — | — | — | — | — | −0.486 | 0.091 | −0.213 | −5.365 | < 0.001 |
| Receiving psychological support (Ref: No) | — | — | — | — | — | — | — | — | — | — | −0.288 | 0.091 | −0.114 | −3.149 | 0.002 | |
| Injury during the earthquake (Ref: No) | −0.185 | 0.089 | −0.078 | −2.084 | 0.038 | |||||||||||
| Model 1 | R = 0.335 | R2 = 0.112 | Adj. R2 = 0.103 | F = 12.369 | p < 0.001 | |||||||||||
| Model 2 | R = 0.478 | R2 = 0.228 | Adj. R2 = 0.214 | F = 15.722 | p < 0.001 | |||||||||||
| Model 3 | R = 0.539 | R2 = 0.290 | Adj. R2 = 0.273 | F = 16.990 | p < 0.001 | |||||||||||
All regression models were checked for multicollinearity (VIF < 5.0) and autocorrelation (Durbin-Watson = 1.979). Model coefficients are validated with 95% confidence intervals within the text
The addition of enabling factors to the second model approximately doubled the model’s explanatory power while maintaining its significance (Adj. R² = 0.214, p < 0.001). In the model where those with income equal to their expenses were taken as a reference, it was found that individuals with income less than their expenses delayed healthcare more, while those with income greater than their expenses showed less delaying behavior. In the model, experiencing problems in accessing healthcare and finding appointments were found to be significant predictors of healthcare delaying behavior. Individuals experiencing difficulties in finding appointments and transportation issues delayed healthcare services more than those who did not experience such problems. However, it was determined that individuals experiencing difficulties in finding a physician showed lower healthcare delay behavior compared to those who did not report such difficulty (p < 0.05) (Table 4).
Adding need factors to the third model further increased the model’s explanatory power (Adj. R² = 0.273). The presence of chronic disease, the need for psychological support, and injury status in the earthquake were found to be significant predictors of healthcare postponement behavior (p < 0.05). In Model 3, the presence of chronic disease was identified as the strongest predictor (β = -0.213, p < 0.001). Compared to their respective reference groups, receiving psychological support (β = −0.114, p = 0.002) and sustained injury during the earthquake (β = −0.078, p = 0.038) were significantly associated with lower levels of healthcare delay behavior (Table 4).
Discussion
This study examined healthcare delay behavior among earthquake survivors living in temporary accommodation centers within the framework of Andersen’s Behavioral Model. The findings were discussed according to predisposing, enabling, and need factors associated with healthcare utilization. Conducted during the long-term recovery period after the earthquake, the study provides evidence regarding the continuing effects of disasters on healthcare access and healthcare-seeking behaviors beyond the acute phase.
This study found that the prevalence of postponing healthcare services after the disaster was 48.83%, indicating that nearly one in two individuals postponed healthcare services despite perceived need. This finding suggests that disruptions in healthcare utilization may continue even during the long-term recovery period after disasters. The literature reports that delays in accessing healthcare services after a disaster and rates of inability to access services are particularly high in the first days to weeks [30] and that this continues to be widespread in the long term [36]. Furthermore, studies conducted in Türkiye show that the prevalence of unmet healthcare needs among the general population ranges from approximately 13% to 25% [7, 36, 37]. This rate is reported to reach 28.4% among the elderly population [38]. Therefore, the findings suggest that earthquake survivors living in temporary accommodation centers may remain vulnerable to healthcare access problems even years after the disaster. Delayed healthcare utilization may negatively affect early diagnosis, treatment continuity, and management of preventable health conditions [39, 40]. These findings highlight the importance of maintaining accessible and continuous primary healthcare services during the post-disaster recovery process.
Predisposing factors were significantly associated with healthcare delay behavior in the present study. Individuals in the younger age groups, particularly those aged 18–34, reported significantly higher healthcare delay behavior compared with older participants aged 65 and over. Similar findings have been reported in previous studies indicating that younger adults are more likely to postpone healthcare utilization [6, 34, 41]. In post-disaster settings, younger individuals may prioritize work responsibilities, family obligations, or daily living concerns over healthcare needs [42]. This finding may also reflect differences in health perception and healthcare-seeking priorities between younger and older individuals reported in previous studies [43]. Older individuals may seek healthcare services more regularly due to increased chronic health conditions and greater perceived need for medical care [41, 44].
Male participants and single individuals also demonstrated higher healthcare delay behavior. Previous studies similarly report that men are more likely to postpone professional healthcare utilization [44, 45]. In addition, studies suggest that women may have more frequent contact with healthcare services and may be more likely to seek professional support when health problems arise [45]. The higher delay behavior observed among single individuals may also be associated with lower levels of social support in healthcare decision-making processes [44, 46]. These findings support the role of sociodemographic characteristics as important determinants of healthcare utilization behaviors.
The inclusion of enabling factors substantially increased the explanatory power of the regression model, emphasizing the importance of healthcare system–related barriers in explaining healthcare delay behavior after disasters. Individuals whose income was lower than their expenses reported significantly higher delay behavior, indicating that financial limitations remain an important barrier to healthcare utilization during the recovery period.
Previous studies conducted both globally and in Türkiye similarly identify economic hardship as a major factor limiting healthcare access [32, 33, 47]. Economic losses experienced after disasters and increased healthcare-related expenditures may contribute to delayed healthcare utilization [48, 49]. In Türkiye, the reduction in public healthcare service capacity and the increased shift toward private health institutions following the February 6 earthquakes may have also exacerbated the economic burden [50].
Difficulty in obtaining appointments and transportation problems were also significant predictors of healthcare delay behavior. These findings indicate that structural barriers within the healthcare system continue to affect healthcare utilization during the long-term recovery period after disasters. Previous studies have similarly reported that reduced healthcare capacity, overloaded appointment systems, and transportation-related barriers contribute to unmet healthcare needs and delayed healthcare utilization following disasters [31, 51–54].
The finding that difficulty in finding a physician was associated with lower healthcare delay behavior should be interpreted cautiously. One possible explanation is that individuals actively searching for a physician may already be in more frequent contact with the healthcare system compared to others. This finding may also reflect the complex nature of healthcare utilization behaviors in post-disaster settings and should be investigated further in future studies.
Need factors also played an important role in explaining healthcare delay behavior.
Participants without chronic diseases demonstrated higher delay behavior than those with chronic diseases. Individuals with chronic conditions may maintain more regular contact with healthcare services due to ongoing treatment and follow-up requirements. Previous studies similarly indicate that perceived health needs and chronic disease management significantly influence healthcare-seeking behaviors [55–57]. In the present study, the presence of chronic disease emerged as one of the strongest predictors in the final regression model, supporting the importance of need-related factors within Andersen’s Behavioral Model.
Participants receiving psychological support demonstrated lower healthcare delay behavior. Previous studies have shown that psychological well-being and psychosocial functioning are associated with healthcare utilization patterns after disasters [58, 59]. Access to psychological support services may facilitate continued engagement with healthcare systems during the recovery period. These findings suggest that psychosocial support services may contribute not only to mental recovery but also to continuity in healthcare utilization after disasters.
In addition, individuals injured during the earthquake demonstrated lower healthcare delay behavior. This finding may be associated with increased healthcare needs and more frequent interaction with healthcare services among injured individuals following disasters [60].
Similarly, participants who had experienced a previous disaster showed lower delay behavior in the earlier models, although this association lost significance after need-related variables were added to the final model. Although the regression model was statistically significant, its explanatory power was moderate, suggesting that additional unmeasured factors may also contribute to healthcare delay behavior.
Overall, the findings demonstrate that healthcare delay behavior among earthquake survivors is shaped by a combination of predisposing, enabling, and need factors. While individual sociodemographic characteristics contribute to healthcare utilization patterns, structural barriers related to healthcare access and ongoing health needs appear to play a more substantial role during the long-term post-disaster recovery period. These findings emphasize the importance of strengthening accessible, continuous, and community-based healthcare services for vulnerable populations affected by disasters. Based on these findings, several targeted and practical intervention programs are recommended for post-disaster healthcare planning. Chronic disease management programs should be implemented in temporary accommodation centers to ensure regular follow-up, medication adherence, and continuity of care for individuals with long-term conditions. Psychosocial support programs should be expanded to reduce avoidance behaviors and strengthen healthcare-seeking motivation. Furthermore, mobile health units and outreach services can be used to reduce transportation-related barriers and improve access to healthcare services for vulnerable populations living in temporary settlements.
Limitations of the study
This study offers a unique contribution as one of the first studies to examine healthcare delay behavior in individuals living in temporary accommodation centers in Türkiye in the post-disaster period. The study evaluates healthcare delay behavior based on Andersen’s Behavioral Model, considering predisposing, facilitating, and need-related factors, thereby distinguishing itself from the limited number of studies in the literature. The study’s contribution to the literature is strengthened by its comprehensive framework addressing the dynamics of access to and use of healthcare services in the long-term post-disaster period. However, the cross-sectional design of the study does not allow for causal interpretations of the relationships between variables.
Furthermore, the fact that the data obtained are based on participants’ self-reports carries the risk of recall bias, which may affect the accuracy of the responses. The use of self-report measures may also lead to potential biases, such as social desirability bias, which may cause participants to underreport or overreport their healthcare delay behaviors.
Another important limitation is the use of a non-probability sampling method and the inclusion of only adult earthquake survivors living in temporary accommodation centers in Hatay province. This may have introduced selection bias and limits the generalizability of the findings to earthquake survivors living in different regions of Türkiye. Additionally, a relatively low internal consistency was observed for the “not taking action” subscale of the Healthcare Demand Procrastination Scale (Cronbach’s Alpha = 0.59). However, this subscale was not utilized independently in any of the primary statistical analyses or multivariate regression models. Instead, all predictive models and evaluations were conducted using the total mean score of the scale, which demonstrated a strong and acceptable overall reliability coefficient (Cronbach’s Alpha = 0.91). Therefore, the lower reliability of this specific subscale does not compromise the validity of the study’s main regression findings, though subscale-level details should be interpreted with caution.
Moreover, the adjusted R² value of 0.273 obtained in the regression analysis indicates that the model explains a moderate proportion of the variance in healthcare delay behavior. This finding suggests that other unmeasured variables may also play a role in explaining healthcare delay behavior.
Accordingly, it is recommended that future studies include variables such as health anxiety, disease severity, post-traumatic psychological symptoms, cultural beliefs, and perceptions of health in the model to explain healthcare delay behavior more comprehensively. Furthermore, monitoring changes in healthcare delay behavior over time using longitudinal designs in the post-disaster period will contribute to a stronger demonstration of causal relationships.
Conclusion
This study shows that earthquake survivors living in temporary accommodation centers in Türkiye continue to delay seeking healthcare even in the late post-disaster period. The results of this research highlight the need to comprehensively address the individual and structural factors that determine access to healthcare in the post-disaster period. This situation demonstrates that disasters do not only cause physical destruction; they also give rise to long-term behavioral and structural health inequalities. The post-disaster recovery process should not be limited to housing and infrastructure repair; it must be restructured with a trauma-sensitive and sustainable health system perspective that facilitates individuals’ access to healthcare. Therefore, it is crucial that disaster management policies include strategies that support long-term health behavior transformation, not just emergency response.
In this context, strengthening primary healthcare services in temporary accommodation areas, improving appointment and referral systems to reduce access barriers, ensuring transportation support for vulnerable groups, and integrating accessible psychosocial support services into routine post-disaster healthcare delivery are recommended. In addition, expanding community-based health services and increasing the availability of relevant medical specialties may further reduce healthcare delay behaviors among disaster-affected populations.
Health professionals should develop psychosocial awareness and access-based programs that support individuals’ health-seeking behaviors in the post-disaster period.
Supplementary Information
Acknowledgements
The authors would like to thank all the volunteers and participants who took part in the data collection for this study.
Abbreviations
- SD
Standard Deviation
- SPSS
Statistical Package for the Social Sciences
Authors’ contributions
Yasemin Gümüş Şekerci: Conceptualization; Methodology; Formal analysis; Writing – original draft; Writing – review & editing; Visualization; Project administration. Gülşah Ayvazoğlu: Data curation; Investigation; Resources; Writing – review & editing; Supervision; Validation. Mustafa Çekiç: Data curation; Investigation; Resources; Writing – review & editing.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Data availability
The datasets generated and/or analyzed during the current study are not publicly available due to privacy and ethical considerations but are available from the corresponding author, Yasemin Gümüş Şekerci (e-mail: yasemin.sekerci@selcuk.edu.tr), on reasonable request.
Declarations
Ethics approval and consent to participate
Ethical approval for the study was obtained from the Scientific Research and Publication Ethics Committee of Social and Humanities Sciences at Hatay Mustafa Kemal University (Decision No: 22, Meeting No: 05, Date: 10.04.2025). In addition, the necessary institutional permissions were obtained from the Hatay Provincial Disaster and Emergency Management Directorate for the temporary accommodation centers where the data collection process will be carried out. The purpose, scope, and voluntary nature of the research were explained to the participants, and a written Informed Consent Form was obtained. For illiterate participants, the study was explained in an understandable manner by the researchers, and informed consent was obtained from their legal guardians. Written permission were obtained from the relevant authors for the measurement tool used. Participation in the study was entirely voluntary, and no coercion or financial incentives were applied to participants. Considering the possible psychological effects of post-disaster conditions, the data collection process was conducted with a trauma-sensitive approach. Participants’ right not to answer questions that might remind them of past disaster experiences was specifically emphasized. Confidentiality and privacy were maintained in all interviews, and the research was conducted in accordance with the ethical principles of the Helsinki Declaration.
Consent for publication
Written informed consent for publication was obtained from the participants.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due to privacy and ethical considerations but are available from the corresponding author, Yasemin Gümüş Şekerci (e-mail: yasemin.sekerci@selcuk.edu.tr), on reasonable request.